Joint Modeling of HCV and HIV Infections among Injecting Drug Users in Italy Using Repeated Cross-Sectional Prevalence Data

Abstract

During their injecting career, injecting drug users (IDUs) are exposed to some infections, like hepatitis C virus (HCV) infection and human immunodeficiency virus (HIV) infection, due to their injecting behavioral risk factors, such as sharing syringes or other paraphernalia containing infected blood, or sexual behavior risk factors. If we consider that these IDUs might belong to a social network of people where these behavioral risk factors are spread, then HCV and HIV infections might be associated at both the individual and the population level. In this paper, we study the association between HCV and HIV infection at the population level using aggregate data. Our aim is to define a hierarchy of structured models with which the association between HCV and HIV infection at population level and the time trend of prevalence can be investigated. The data analyzed in the paper are “diagnostic testing data,” which consist of repeated cross-sectional prevalence measurements from 1998 to 2006 for HCV and HIV infection, obtained from a sample of 515 drug treatment centers spread among the 20 regions in Italy, where subjects went for a serum diagnostic test. Since we do not have any individual data, it is not possible to relate these prevalence data to socio-demographic or behavioral risk data. Each region defines a cluster with repeated prevalence data for HCV and HIV infection over time. Several modeling approaches, such as generalized linear mixed models (GLMMs) and hierarchical Bayesian models are applied to the data. First, we test different covariance structures for the region-specific random effects in the GLMM context; second, a hierarchical Bayesian model is used to refit the best GLMM in order to obtain the posterior distribution for the parameters of primary interest. We found that the correlation at population level between HCV and HIV is approximately 0.68 and the prevalence of the two infections generally decreased over the years, compared to the situation in 1998.

Statement on a conceptual framework for bovine tuberculosis

“…In general, it is expected that the risk of infection might depend on social behavioural factors. Examples of such dependency have been studied for hepatitis C among injecteddrug users (Mathëi et al, 2006;del Fava et al, 2011). Mathëi and colleagues presented the way to model p i1 as a function of injection time and social behavioural risk factors (such as sharing syringes and other paraphernalia).…”

Estimating the force of infection for HCV in injecting drug users using interval-censored data

“…Several attempts have been made to estimate the FOI of HCV in the IDU context based on cross-sectional data [6][7][8][9][10][11]. All authors assumed a parametric function for the prevalence and the FOI, either assessing the impact of covariates or taking into account the association with other viruses.…”

“…The estimation of the FOI for HCV in the IDU population was based previously on cross-sectional data [6][7][8][9][10][11]. The major contribution of this paper is the estimation of the FOI for HCV in IDUs using a large cohort study, with more than 25 years of followup, while assessing the impact of self-reported behavioural risk factors (injection frequency, type of drug injected, sharing of syringes) using an adequate statistical model.…”

Joint Modeling of Singleton Preterm Birth and Perinatal Death Using Birth Registry Cohort Data in Northern Tanzania

“…Joint modeling, particularly using the random effects approach, have been previously applied to clinical outcomes such as HIV and HCV ( 25 , 26 ), hearing thresholds ( 27 , 28 ), and body mass index with other clinical targets among diabetic patients ( 29 ). The application of these methods to pregnancy-related adverse outcomes is limited.…”